Use graph RAG to produce comprehensive, context-aware answers from generative AI models

Generative AI models like large language models (LLMs) often struggle with accuracy and reliability. Without proper context, they can produce hallucinations containing inaccuracies.

Standard retrieval-augmented generation (RAG) approaches miss complex relationships and multi-step connections across your enterprise data, limiting the depth and relevance of AI-generated insights.

Improve gen AI results with RAG

Graph retrieval-augmented generation (Graph RAG or GRAG) combines the strengths of knowledge graphs with generative AI to deliver superior results:

Core Capabilities:

• Reduce hallucinations by grounding LLM responses in vetted, relationship-rich enterprise data
• Improve accuracy by capturing multi-step relationships and contextual dependencies that standard RAG misses
• Uncover hidden insights by finding connections across large disparate datasets
• Enhance explainability by tracing every response back to source data and underlying logic
• Adapt in real time using up-to-date data, analytics, and context-aware filtering
• Scale across domains with enterprise-ready, governed knowledge graphs

Knowledge graphs capture and represent relationships between data points and conceptual entities, enabling AI models to find all relevant information and produce more useful insights than RAG alone.

Ready to enhance your AI with graph RAG? Download the fact sheet to learn more.